Tootfinder

Biased-Attention Guided Risk Prediction for Safe Decision-Making at Unsignalized Intersections
Chengyang Dong, Nan Guo
https://arxiv.org/abs/2510.12428 https://

Biased-Attention Guided Risk Prediction for Safe Decision-Making at Unsignalized Intersections
Autonomous driving decision-making at unsignalized intersections is highly challenging due to complex dynamic interactions and high conflict risks. To achieve proactive safety control, this paper proposes a deep reinforcement learning (DRL) decision-making framework integrated with a biased attention mechanism. The framework is built upon the Soft Actor-Critic (SAC) algorithm. Its core innovation lies in the use of biased attention to construct a traffic risk predictor. This predictor assesses …

Discovering interpretable piecewise nonlinear model predictive control laws via symbolic decision trees
Ilias Mitrai
https://arxiv.org/abs/2510.10411 https://

Discovering interpretable piecewise nonlinear model predictive control laws via symbolic decision trees
In this paper, we propose symbolic decision trees as surrogate models for approximating model predictive control laws. The proposed approach learns simultaneously the partition of the input domain (splitting logic) as well as local nonlinear expressions for predicting the control action leading to interpretable piecewise nonlinear control laws. The local nonlinear expressions are determined by the learning problem and are modeled using a set of basis functions. The learning task is posed as a m…

The national guard troops Donald Trump sent to Illinois can remain in the state and under federal control
but can’t be deployed, an appeals court ruled on Saturday.
The appeals court granted a pause in the case until it can hear further arguments.
The decision to prevent the troops from protecting federal property or going on patrol comes after federal judge April Perry ruled on Thursday to temporarily block the national guard deployment for at least two weeks,
findin…

Degradation-Aware Model Predictive Control for Battery Swapping Stations under Energy Arbitrage
Ruochen Li (Department of Systems Engineering, City University of Hong Kong, Kowloon, Hong Kong, China), Zhichao Chen (Department of Systems Engineering, City University of Hong Kong, Kowloon, Hong Kong, China), Zhaoting Zhang (Department of Systems Engineering, City University of Hong Kong, Kowloon, Hong Kong, China), Renjie Guo (Department of Decision Analytics and Operations, City Univers…

Degradation-Aware Model Predictive Control for Battery Swapping Stations under Energy Arbitrage
Battery swapping stations (BSS) offer a fast and scalable alternative to conventional electric vehicle (EV) charging, gaining growing policy support worldwide. However, existing BSS control strategies typically rely on heuristics or low-fidelity degradation models, limiting profitability and service level. This paper proposes BSS-MPC: a real-time, degradation-aware Model Predictive Control (MPC) framework for BSS operations to trade off economic incentives from energy market arbitrage and long-…

Red Lines and Grey Zones in the Fog of War: Benchmarking Legal Risk, Moral Harm, and Regional Bias in Large Language Model Military Decision-Making
Toby Drinkall
https://arxiv.org/abs/2510.03514

Red Lines and Grey Zones in the Fog of War: Benchmarking Legal Risk, Moral Harm, and Regional Bias in Large Language Model Military Decision-Making
As military organisations consider integrating large language models (LLMs) into command and control (C2) systems for planning and decision support, understanding their behavioural tendencies is critical. This study develops a benchmarking framework for evaluating aspects of legal and moral risk in targeting behaviour by comparing LLMs acting as agents in multi-turn simulated conflict. We introduce four metrics grounded in International Humanitarian Law (IHL) and military doctrine: Civilian Tar…

Decentralised Blockchain Management Through Digital Twins
Georgios Diamantopoulos, Nikos Tziritas, Rami Bahsoon, Georgios Theodoropoulos
https://arxiv.org/abs/2510.07901 https:/…

Decentralised Blockchain Management Through Digital Twins
The necessity of blockchain systems to remain decentralised limits current solutions to blockchain governance and dynamic management, forcing a trade-off between control and decentralisation. In light of the above, this work proposes a dynamic and decentralised blockchain management mechanism based on digital twins. To ensure decentralisation, the proposed mechanism utilises multiple digital twins that the system's stakeholders control. To facilitate decentralised decision-making, the twins are…

When Should Users Check? A Decision-Theoretic Model of Confirmation Frequency in Multi-Step AI Agent Tasks
Jieyu Zhou, Aryan Roy, Sneh Gupta, Daniel Weitekamp, Christopher J. MacLellan
https://arxiv.org/abs/2510.05307

When Should Users Check? A Decision-Theoretic Model of Confirmation Frequency in Multi-Step AI Agent Tasks
Existing AI agents typically execute multi-step tasks autonomously and only allow user confirmation at the end. During execution, users have little control, making the confirm-at-end approach brittle: a single error can cascade and force a complete restart. Confirming every step avoids such failures, but imposes tedious overhead. Balancing excessive interruptions against costly rollbacks remains an open challenge. We address this problem by modeling confirmation as a minimum time scheduling pro…

🌍 The climate crisis demands urgent action. But which actions are best?
Decision makers face tough trade-offs:
Policy A lowers emissions at home but increases reliance on imports.
Policy B cuts emissions long-term but raises unemployment short-term.
Policy C boosts jobs now but increases emissions in the near term.
None of these choices are simple. A policy that looks good locally may increase global emissions, or its effects may depend on what other countries d…

Learning to Generate Object Interactions with Physics-Guided Video Diffusion
David Romero, Ariana Bermudez, Hao Li, Fabio Pizzati, Ivan Laptev
https://arxiv.org/abs/2510.02284 h…

Learning to Generate Object Interactions with Physics-Guided Video Diffusion
Recent models for video generation have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond their creative potential, such models also hold promise as world simulators for robotics and embodied decision making. Despite strong advances, however, current approaches still struggle to generate physically plausible object interactions and lack physics-grounded control mechanisms. To address this limitation, we introduce KineMask, an approach fo…

Sensors in viticulture: functions, benefits, and data-driven insights
Milan Milenkovic
https://arxiv.org/abs/2510.03000 https://arxiv.org/pdf/2510.03000

Sensors in viticulture: functions, benefits, and data-driven insights
Use of sensor and related analytical predictions can be a powerful tool in providing data-informed input to viticulturalists' decision process, complementing their vineyard observations and intuition. Their up-to-date measurements, predictions, and alerts offer actionable insights and suggestions for managing key vineyard operations, such as irrigation, disease and pest control, canopy management, and harvest timing. In many cases, anticipatory interventions can mitigate risks before problems b…

Multi-Actor Multi-Critic Deep Deterministic Reinforcement Learning with a Novel Q-Ensemble Method
Andy Wu, Chun-Cheng Lin, Rung-Tzuo Liaw, Yuehua Huang, Chihjung Kuo, Chia Tong Weng
https://arxiv.org/abs/2510.01083

Multi-Actor Multi-Critic Deep Deterministic Reinforcement Learning with a Novel Q-Ensemble Method
Reinforcement learning has gathered much attention in recent years due to its rapid development and rich applications, especially on control systems and robotics. When tackling real-world applications with reinforcement learning method, the corresponded Markov decision process may have huge discrete or even continuous state/action space. Deep reinforcement learning has been studied for handling these issues through deep learning for years, and one promising branch is the actor-critic architectu…

From the article: "He added: “It’s something where it’s moving very quickly and people don’t necessarily have time to absorb it or figure out what to do.”"
That impersonal, natural "it" is moving - Not the people developing the models and selling them (or inflicting them), not the wealthy investors demanding market share, not the sci-fi addled techbros (Muskrats?) imagining that if only we get AGI all problems will be solved tomorrow, - all wanting to be first. Oh…

From the article: "He added: “It’s something where it’s moving very quickly and people don’t necessarily have time to absorb it or figure out what to do.”"
That impersonal, natural "it" is moving - Not the people developing the models and selling them (or inflicting them), not the wealthy investors demanding market share, not the sci-fi addled techbros (Muskrats?) imagining that if only we get AGI all problems will be solved tomorrow, - all wanting to be first. Oh…

Cory Doctorow gets it – Time to download privacy apps now
The Dictator/Fascist/Authoritarian playbook is well understood. Surveillance is a key part of the effort to dominate and punish individuals who engage in legal dissent or opposition. The recent decision by Apple and Google to remove the ICEBlock application from their app stores is a good example of how this control plays out.
Our mobile phones are the main platform that we use to send and receive text and email messages.…

Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
Sigmund Hennum H{\o}eg, Aksel Vaaler, Chaoqi Liu, Olav Egeland, Yilun Du
https://arxiv.org/abs/2509.21983 http…

Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
Constructing robots to accomplish long-horizon tasks is a long-standing challenge within artificial intelligence. Approaches using generative methods, particularly Diffusion Models, have gained attention due to their ability to model continuous robotic trajectories for planning and control. However, we show that these models struggle with long-horizon tasks that involve complex decision-making and, in general, are prone to confusing different modes of behavior, leading to failure. To remedy thi…

Bridging Control Variates and Regression Adjustment in A/B Testing: From Design-Based to Model-Based Frameworks
Yu Zhang, Bokui Wan, Yongli Qin
https://arxiv.org/abs/2509.13944 …

Bridging Control Variates and Regression Adjustment in A/B Testing: From Design-Based to Model-Based Frameworks
A B testing serves as the gold standard for large scale, data driven decision making in online businesses. To mitigate metric variability and enhance testing sensitivity, control variates and regression adjustment have emerged as prominent variance reduction techniques, leveraging pre experiment data to improve estimator performance. Over the past decade, these methods have spawned numerous derivatives, yet their theoretical connections and comparative properties remain underexplored. In this p…

Understanding Mode Switching in Human-AI Collaboration: Behavioral Insights and Predictive Modeling
Avinash Ajit Nargund, Arthur Caetano, Kevin Yang, Rose Yiwei Liu, Philip Tezaur, Kriteen Shrestha, Qisen Pan, Tobias H\"ollerer, Misha Sra
https://arxiv.org/abs/2509.20666

Understanding Mode Switching in Human-AI Collaboration: Behavioral Insights and Predictive Modeling
Human-AI collaboration is typically offered in one of two of user control levels: guidance, where the AI provides suggestions and the human makes the final decision, and delegation, where the AI acts autonomously within user-defined constraints. Systems that integrate both modes, common in robotic surgery or driving assistance, often overlook shifts in user preferences within a task in response to factors like evolving trust, decision complexity, and perceived control. In this work, we investig…

Nexstar and Sinclair control 63 of ABC's 205 affiliate stations; Nexstar's proposed $6B merger with Tegna, which requires FCC approval, could give it 13 more (New York Times)
https://www.nytimes.com/interactive/2025/09/19/business/media/abc-nexst…

How Nexstar’s Proposed TV Merger Is Tied to Jimmy Kimmel’s Suspension
Behind ABC’s decision was a display of how influential members of the Trump administration have become in shaping how media companies operate.

Series A, Episode 11 - Bounty
SARKOFF: To my decision? Of course not.
BLAKE: No, of course not. [Checks watch a final time then looks up] Right. Stay here, please. [Exits]
SARKOFF: Where else would I go? This is all I have left.
https://blake.torpidity.net/m/111/268 B7B4

Agentic UAVs: LLM-Driven Autonomy with Integrated Tool-Calling and Cognitive Reasoning
Anis Koubaa, Khaled Gabr
https://arxiv.org/abs/2509.13352 https://ar…

Agentic UAVs: LLM-Driven Autonomy with Integrated Tool-Calling and Cognitive Reasoning
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in defense, surveillance, and disaster response, yet most systems remain confined to SAE Level 2--3 autonomy. Their reliance on rule-based control and narrow AI restricts adaptability in dynamic, uncertain missions. Existing UAV frameworks lack context-aware reasoning, autonomous decision-making, and ecosystem-level integration; critically, none leverage Large Language Model (LLM) agents with tool-calling for real-time knowledge access. …

Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control
Max Studt, Georg Schildbach
https://arxiv.org/abs/2509.15799 https://arxiv.o…

Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control
Achieving safe and coordinated behavior in dynamic, constraint-rich environments remains a major challenge for learning-based control. Pure end-to-end learning often suffers from poor sample efficiency and limited reliability, while model-based methods depend on predefined references and struggle to generalize. We propose a hierarchical framework that combines tactical decision-making via reinforcement learning (RL) with low-level execution through Model Predictive Control (MPC). For the case o…

Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
Shengbo Wang, Nian Si
https://arxiv.org/abs/2509.14203 https://a…

Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
Learning and optimal control under robust Markov decision processes (MDPs) have received increasing attention, yet most existing theory, algorithms, and applications focus on finite-horizon or discounted models. The average-reward formulation, while natural in many operations research and management contexts, remains underexplored. This is primarily because the dynamic programming foundations are technically challenging and only partially understood, with several fundamental questions remaining…

Reinforcement Learning-Based Market Making as a Stochastic Control on Non-Stationary Limit Order Book Dynamics
Rafael Zimmer, Oswaldo Luiz do Valle Costa
https://arxiv.org/abs/2509.12456

Reinforcement Learning-Based Market Making as a Stochastic Control on Non-Stationary Limit Order Book Dynamics
Reinforcement Learning has emerged as a promising framework for developing adaptive and data-driven strategies, enabling market makers to optimize decision-making policies based on interactions with the limit order book environment. This paper explores the integration of a reinforcement learning agent in a market-making context, where the underlying market dynamics have been explicitly modeled to capture observed stylized facts of real markets, including clustered order arrival times, non-stati…

Rich State Observations Empower Reinforcement Learning to Surpass PID: A Drone Ball Balancing Study
Mingjiang Liu, Hailong Huang
https://arxiv.org/abs/2509.21122 https://…

Rich State Observations Empower Reinforcement Learning to Surpass PID: A Drone Ball Balancing Study
This paper addresses a drone ball-balancing task, in which a drone stabilizes a ball atop a movable beam through cable-based interaction. We propose a hierarchical control framework that decouples high-level balancing policy from low-level drone control, and train a reinforcement learning (RL) policy to handle the high-level decision-making. Simulation results show that the RL policy achieves superior performance compared to carefully tuned PID controllers within the same hierarchical structure…

Explainable AI for Maritime Autonomous Surface Ships (MASS): Adaptive Interfaces and Trustworthy Human-AI Collaboration
Zhuoyue Zhang, Haitong Xu
https://arxiv.org/abs/2509.15959

Explainable AI for Maritime Autonomous Surface Ships (MASS): Adaptive Interfaces and Trustworthy Human-AI Collaboration
Autonomous navigation in maritime domains is accelerating alongside advances in artificial intelligence, sensing, and connectivity. Opaque decision-making and poorly calibrated human-automation interaction remain key barriers to safe adoption. This article synthesizes 100 studies on automation transparency for Maritime Autonomous Surface Ships (MASS) spanning situation awareness (SA), human factors, interface design, and regulation. We (i) map the Guidance-Navigation-Control stack to shore-base…

Agentic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration
Liangxuan Guo, Bin Zhu, Qingqian Tao, Kangning Liu, Xun Zhao, Xianzhe Qin, Jin Gao, Guangfu Hao
https://arxiv.org/abs/2509.11067

Agentic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration
Autonomous agents for desktop automation struggle with complex multi-step tasks due to poor coordination and inadequate quality control. We introduce \textsc{Agentic Lybic}, a novel multi-agent system where the entire architecture operates as a finite-state machine (FSM). This core innovation enables dynamic orchestration. Our system comprises four components: a Controller, a Manager, three Workers (Technician for code-based operations, Operator for GUI interactions, and Analyst for decision su…

Pure Vision Language Action (VLA) Models: A Comprehensive Survey
Dapeng Zhang, Jin Sun, Chenghui Hu, Xiaoyan Wu, Zhenlong Yuan, Rui Zhou, Fei Shen, Qingguo Zhou
https://arxiv.org/abs/2509.19012

Pure Vision Language Action (VLA) Models: A Comprehensive Survey
The emergence of Vision Language Action (VLA) models marks a paradigm shift from traditional policy-based control to generalized robotics, reframing Vision Language Models (VLMs) from passive sequence generators into active agents for manipulation and decision-making in complex, dynamic environments. This survey delves into advanced VLA methods, aiming to provide a clear taxonomy and a systematic, comprehensive review of existing research. It presents a comprehensive analysis of VLA application…

Not All Accuracy Is Equal: Prioritizing Diversity in Infectious Disease Forecasting
Carson Dudley, Marisa Eisenberg
https://arxiv.org/abs/2509.21191 https://

Not All Accuracy Is Equal: Prioritizing Diversity in Infectious Disease Forecasting
Ensemble forecasts have become a cornerstone of large-scale disease response, underpinning decision making at agencies such as the US Centers for Disease Control and Prevention (CDC). Their growing use reflects the goal of combining multiple models to improve accuracy and stability versus using a single model. However, recent experience shows these benefits are not guaranteed. During the COVID-19 pandemic, the CDC's multi-model forecasting ensemble outperformed the best single model by only 1%,…

Replaced article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/1]:
- Optimal Control of Markov Decision Processes for Efficiency with Linear Temporal Logic Tasks
Yu Chen, Xuanyuan Yin, Shaoyuan Li, Xiang Yin